mirror of
https://github.com/moses-smt/mosesdecoder.git
synced 2024-09-11 11:25:40 +03:00
217 lines
7.0 KiB
C++
217 lines
7.0 KiB
C++
/* Efficient left and right language model state for sentence fragments.
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* Intended usage:
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* Store ChartState with every chart entry.
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* To do a rule application:
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* 1. Make a ChartState object for your new entry.
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* 2. Construct RuleScore.
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* 3. Going from left to right, call Terminal or NonTerminal.
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* For terminals, just pass the vocab id.
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* For non-terminals, pass that non-terminal's ChartState.
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* If your decoder expects scores inclusive of subtree scores (i.e. you
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* label entries with the highest-scoring path), pass the non-terminal's
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* score as prob.
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* If your decoder expects relative scores and will walk the chart later,
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* pass prob = 0.0.
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* In other words, the only effect of prob is that it gets added to the
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* returned log probability.
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* 4. Call Finish. It returns the log probability.
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*
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* There's a couple more details:
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* Do not pass <s> to Terminal as it is formally not a word in the sentence,
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* only context. Instead, call BeginSentence. If called, it should be the
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* first call after RuleScore is constructed (since <s> is always the
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* leftmost).
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*
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* If the leftmost RHS is a non-terminal, it's faster to call BeginNonTerminal.
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*
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* Hashing and sorting comparison operators are provided. All state objects
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* are POD. If you intend to use memcmp on raw state objects, you must call
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* ZeroRemaining first, as the value of array entries beyond length is
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* otherwise undefined.
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*
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* Usage is of course not limited to chart decoding. Anything that generates
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* sentence fragments missing left context could benefit. For example, a
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* phrase-based decoder could pre-score phrases, storing ChartState with each
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* phrase, even if hypotheses are generated left-to-right.
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*/
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#ifndef LM_LEFT_H
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#define LM_LEFT_H
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#include "lm/max_order.hh"
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#include "lm/state.hh"
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#include "lm/return.hh"
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#include "util/murmur_hash.hh"
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#include <algorithm>
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namespace lm {
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namespace ngram {
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template <class M> class RuleScore {
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public:
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explicit RuleScore(const M &model, ChartState &out) : model_(model), out_(&out), left_done_(false), prob_(0.0) {
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out.left.length = 0;
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out.right.length = 0;
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}
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void BeginSentence() {
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out_->right = model_.BeginSentenceState();
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// out_->left is empty.
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left_done_ = true;
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}
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void Terminal(WordIndex word) {
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State copy(out_->right);
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FullScoreReturn ret(model_.FullScore(copy, word, out_->right));
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if (left_done_) { prob_ += ret.prob; return; }
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if (ret.independent_left) {
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prob_ += ret.prob;
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left_done_ = true;
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return;
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}
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out_->left.pointers[out_->left.length++] = ret.extend_left;
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prob_ += ret.rest;
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if (out_->right.length != copy.length + 1)
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left_done_ = true;
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}
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// Faster version of NonTerminal for the case where the rule begins with a non-terminal.
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void BeginNonTerminal(const ChartState &in, float prob = 0.0) {
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prob_ = prob;
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*out_ = in;
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left_done_ = in.left.full;
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}
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void NonTerminal(const ChartState &in, float prob = 0.0) {
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prob_ += prob;
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if (!in.left.length) {
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if (in.left.full) {
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for (const float *i = out_->right.backoff; i < out_->right.backoff + out_->right.length; ++i) prob_ += *i;
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left_done_ = true;
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out_->right = in.right;
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}
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return;
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}
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if (!out_->right.length) {
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out_->right = in.right;
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if (left_done_) {
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prob_ += model_.UnRest(in.left.pointers, in.left.pointers + in.left.length, 1);
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return;
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}
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if (out_->left.length) {
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left_done_ = true;
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} else {
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out_->left = in.left;
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left_done_ = in.left.full;
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}
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return;
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}
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float backoffs[KENLM_MAX_ORDER - 1], backoffs2[KENLM_MAX_ORDER - 1];
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float *back = backoffs, *back2 = backoffs2;
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unsigned char next_use = out_->right.length;
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// First word
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if (ExtendLeft(in, next_use, 1, out_->right.backoff, back)) return;
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// Words after the first, so extending a bigram to begin with
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for (unsigned char extend_length = 2; extend_length <= in.left.length; ++extend_length) {
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if (ExtendLeft(in, next_use, extend_length, back, back2)) return;
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std::swap(back, back2);
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}
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if (in.left.full) {
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for (const float *i = back; i != back + next_use; ++i) prob_ += *i;
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left_done_ = true;
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out_->right = in.right;
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return;
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}
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// Right state was minimized, so it's already independent of the new words to the left.
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if (in.right.length < in.left.length) {
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out_->right = in.right;
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return;
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}
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// Shift exisiting words down.
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for (WordIndex *i = out_->right.words + next_use - 1; i >= out_->right.words; --i) {
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*(i + in.right.length) = *i;
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}
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// Add words from in.right.
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std::copy(in.right.words, in.right.words + in.right.length, out_->right.words);
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// Assemble backoff composed on the existing state's backoff followed by the new state's backoff.
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std::copy(in.right.backoff, in.right.backoff + in.right.length, out_->right.backoff);
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std::copy(back, back + next_use, out_->right.backoff + in.right.length);
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out_->right.length = in.right.length + next_use;
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}
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float Finish() {
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// A N-1-gram might extend left and right but we should still set full to true because it's an N-1-gram.
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out_->left.full = left_done_ || (out_->left.length == model_.Order() - 1);
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return prob_;
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}
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void Reset() {
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prob_ = 0.0;
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left_done_ = false;
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out_->left.length = 0;
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out_->right.length = 0;
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}
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void Reset(ChartState &replacement) {
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out_ = &replacement;
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Reset();
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}
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private:
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bool ExtendLeft(const ChartState &in, unsigned char &next_use, unsigned char extend_length, const float *back_in, float *back_out) {
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ProcessRet(model_.ExtendLeft(
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out_->right.words, out_->right.words + next_use, // Words to extend into
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back_in, // Backoffs to use
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in.left.pointers[extend_length - 1], extend_length, // Words to be extended
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back_out, // Backoffs for the next score
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next_use)); // Length of n-gram to use in next scoring.
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if (next_use != out_->right.length) {
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left_done_ = true;
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if (!next_use) {
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// Early exit.
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out_->right = in.right;
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prob_ += model_.UnRest(in.left.pointers + extend_length, in.left.pointers + in.left.length, extend_length + 1);
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return true;
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}
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}
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// Continue scoring.
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return false;
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}
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void ProcessRet(const FullScoreReturn &ret) {
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if (left_done_) {
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prob_ += ret.prob;
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return;
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}
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if (ret.independent_left) {
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prob_ += ret.prob;
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left_done_ = true;
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return;
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}
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out_->left.pointers[out_->left.length++] = ret.extend_left;
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prob_ += ret.rest;
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}
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const M &model_;
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ChartState *out_;
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bool left_done_;
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float prob_;
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};
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} // namespace ngram
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} // namespace lm
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#endif // LM_LEFT_H
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